Counting inventory items using image analysis

A computing system, comprising: a processor; and a memory coupled to the processor and storing program instructions that when executed by the processor cause the processor to at least: receive from a first camera a first image of an inventory location, wherein the first image includes a representation of a plurality of inventory items located at the inventory location; determine from an inventory location data store, an item type corresponding to the inventory location; segment the first image into a plurality of image segments; select a first histogram of oriented gradients (“HOG”) model for a first image segment of the plurality of image segments and a second HOG model for a second image segment of the plurality of image segments, wherein: the first HOG model corresponds to the item type and is representative of a second image of an item of the item type obtained at a first distance or at a first item orientation; the second HOG model corresponds to the item type and is representative of a third image of the item of the item type obtained at a second distance or at a second orientation; process the first image to: generate a first plurality of inventory item feature vectors corresponding to the first image segment, and generate a second plurality of inventory item feature vectors corresponding to the second image segment; compare the first plurality of inventory item feature vectors with the first HOG model; compare the second plurality of inventory item feature vectors with the second HOG model; and count a number of the first plurality of inventory item feature vectors that are substantially similar to the first HOG model and the number of the second plurality of inventory item feature vectors that are substantially similar to the second HOG model, wherein the count is representative of a quantity of items at the inventory location.

A computing system comprising: a processor; and a memory coupled to the processor and storing program instructions that when executed by the processor cause the processor to at least: receive from a first camera a first image of an inventory location, wherein the first image includes a representation of a plurality of inventory items located at the inventory location; determine from an inventory location data store, an item type corresponding to the inventory location; select a first histogram of oriented gradients (“HOG”) model and a second HOG model, wherein: the first HOG model corresponds to the item type and is representative of a second image of an item of the item type obtained at a first distance or at a first item orientation; the second HOG model corresponds to the item type and is representative of a third image of the item of the item type obtained at a second distance or at a second orientation; process the first image to generate a plurality of inventory item feature vectors; compare the plurality of inventory item feature vectors with each of the first HOG model and the second HOG model; determine a count of a number of the plurality of inventory item feature vectors that are substantially similar to at least one of the first HOG model or the second HOG model; alter at least one of a rotation of the first image or a magnification level of the first image; process the altered first image to generate a second plurality of inventory item feature vectors; compare each of the second plurality of inventory item feature vectors with each of the first HOG model and the second HOG model; and determine a second count of a second number of the second plurality of inventory item feature vectors that are substantially similar to at least one of the first HOG model or the second HOG model, wherein a sum of the count and the second count is representative of a quantity of items at the inventory location.

A computer-implemented method for counting items at an inventory location, comprising: under control of one or more computing systems configured with executable instructions, receiving from a camera an image of a plurality of items positioned at an inventory location, receiving depth information for each pixel of a plurality of pixels included in the image, wherein the depth information for each pixel represents a distance between an object represented in the image and the camera; generating a point cloud representative of at least a portion of the image and based at least in part on the depth information; determining a cluster boundary range based at least in part on item information stored in an item information data store, wherein the item information corresponds to the plurality of items positioned at the inventory location, wherein a base of each cluster boundary corresponds to a position of an inventory shelf with respect to each point of the point cloud; determining a plurality of clusters of points in the point cloud, each cluster representative of at least a portion of an item of the plurality of items; and determining an item count based at least in part on a number of clusters.

A computing system, comprising: a processor; and a memory coupled to the processor and storing program instructions that when executed by the processor causes the processor to at least: receive from a camera an image of an inventory location; obtain from an item information data store, item information corresponding to an item type of an item located at the inventory location; process the image to determine a plurality of feature vectors; compare each of the plurality of feature vectors with each of a plurality of model feature vectors included in the item information; determine a number of feature vectors that correspond with at least one of the plurality of model feature vectors; and determine a feature vector position corresponding with at least one of the plurality of model feature vectors; determine that a distance between the feature vector position and an inventory shelf represented in the image varies with respect to the feature vector position; and determine an item count of items located at the inventory location based at least in part on the number of feature vectors that correspond with at least one of the plurality of model feature vectors, and further based at least in part on the feature vector position and the distance.

A computing system, comprising: a processor; and a memory coupled to the processor and storing program instructions that when executed by the processor causes the processor to at least: receive from a camera an image of an inventory location; determine item information corresponding to an item type of an item located at the inventory location; process the image to determine a feature vector; determine that the feature vector corresponds with a model feature vector; determine a feature vector position of the feature vector; determine a distance between the feature vector position and an inventory shelf of the inventory location; and determine an item count of items located at the inventory location based at least in part on the feature vector position and the distance.